Author Correction: Evaluation of deep learning in non-coding RNA classification

Abstract Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs can provide valuable insights into their biological functions. Existing computational methods for predicting lncRNA subcellular localization use k-mer features to encode lncRNA sequences. However, the sequence order information is lost by using only k-mer features. We proposed a deep learning framework, DeepLncLoc, to predict lncRNA subcellular localization. In DeepLncLoc, we introduced a new subsequence embedding method that keeps the order information of lncRNA sequences. The subsequence embedding method first divides a sequence into some consecutive subsequences and then extracts the patterns of each subsequence, last combines these patterns to obtain a complete representation of the lncRNA sequence. After that, a text convolutional neural network is employed to learn high-level features and perform the prediction task. Compared with traditional machine learning models, popular representation methods and existing predictors, DeepLncLoc achieved better performance, which shows that DeepLncLoc could effectively predict lncRNA subcellular localization. Our study not only presented a novel computational model for predicting lncRNA subcellular localization but also introduced a new subsequence embedding method which is expected to be applied in other sequence-based prediction tasks. The DeepLncLoc web server is freely accessible at http://bioinformatics.csu.edu.cn/DeepLncLoc/, and source code and datasets can be downloaded from https://github.com/CSUBioGroup/DeepLncLoc.

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ncRDeep: Non-coding RNA classification with convolutional neural network

Computational Biology and Chemistry ◽

10.1016/j.compbiolchem.2020.107364 ◽

2020 ◽

Vol 88 ◽

pp. 107364

Author(s):

Tuvshinbayar Chantsalnyam ◽

Dae Yeong Lim ◽

Hilal Tayara ◽

Kil To Chong

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Non Coding Rna ◽

Rna Classification

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A Novel Integrative Approach for Non-coding RNA Classification Based on Deep Learning

Current Bioinformatics ◽

10.2174/1574893614666191105160633 ◽

2020 ◽

Vol 15 (4) ◽

pp. 338-348

Author(s):

Abdelbasset Boukelia ◽

Anouar Boucheham ◽

Meriem Belguidoum ◽

Mohamed Batouche ◽

Farida Zehraoui ◽

...

Keyword(s):

Deep Learning ◽

Structural Information ◽

Optimization Technique ◽

Critical Issue ◽

Human Diseases ◽

Integrative Approach ◽

Computational Tools ◽

Novel Approach ◽

Non Coding Rna ◽

Highly Correlated

Background: Molecular biomarkers show new ways to understand many disease processes. Noncoding RNAs as biomarkers play a crucial role in several cellular activities, which are highly correlated to many human diseases especially cancer. The classification and the identification of ncRNAs have become a critical issue due to their application, such as biomarkers in many human diseases. Objective: Most existing computational tools for ncRNA classification are mainly used for classifying only one type of ncRNA. They are based on structural information or specific known features. Furthermore, these tools suffer from a lack of significant and validated features. Therefore, the performance of these methods is not always satisfactory. Methods: We propose a novel approach named imCnC for ncRNA classification based on multisource deep learning, which integrates several data sources such as genomic and epigenomic data to identify several ncRNA types. Also, we propose an optimization technique to visualize the extracted features pattern from the multisource CNN model to measure the epigenomics features of each ncRNA type. Results: The computational results using a dataset of 16 human ncRNA classes downloaded from RFAM show that imCnC outperforms the existing tools. Indeed, imCnC achieved an accuracy of 94,18%. In addition, our method enables to discover new ncRNA features using an optimization technique to measure and visualize the features pattern of the imCnC classifier.

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EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA–protein interaction prediction

BMC Bioinformatics ◽

10.1186/s12859-021-04069-9 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Jingjing Wang ◽

Yanpeng Zhao ◽

Weikang Gong ◽

Yang Liu ◽

Mei Wang ◽

...

Keyword(s):

Deep Learning ◽

Protein Interaction ◽

Protein Interactions ◽

Tertiary Structure ◽

Short Term Memory ◽

Biological Information ◽

Sequence Features ◽

Multi Scale ◽

Non Coding Rna ◽

Increasing Demand

Abstract Background Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA–protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA–protein interactions. Results In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA–protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ensemble deep learning model, which combined convolutional neural network (CNN) to learn dominating biological information with bi-directional long short-term memory network (BLSTM) to capture long-range dependencies among the features identified by the CNN. Compared with other state-of-the-art methods under five-fold cross-validation, EDLMFC shows the best performance with accuracy of 93.8%, 89.7%, and 86.1% on RPI1807, NPInter v2.0, and RPI488 datasets, respectively. The results of the independent test demonstrated that EDLMFC can effectively predict potential ncRNA–protein interactions from different organisms. Furtherly, EDLMFC is also shown to predict hub ncRNAs and proteins presented in ncRNA–protein networks of Mus musculus successfully. Conclusions In general, our proposed method EDLMFC improved the accuracy of ncRNA–protein interaction predictions and anticipated providing some helpful guidance on ncRNA functions research. The source code of EDLMFC and the datasets used in this work are available at https://github.com/JingjingWang-87/EDLMFC.

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ncRDense: A novel computational approach for classification of non-coding RNA family by deep learning

Genomics ◽

10.1016/j.ygeno.2021.07.004 ◽

2021 ◽

Author(s):

Tuvshinbayar Chantsalnyam ◽

Arslan Siraj ◽

Hilal Tayara ◽

Kil To Chong

Keyword(s):

Deep Learning ◽

Computational Approach ◽

Non Coding Rna

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DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding

10.1101/2021.03.13.435245 ◽

2021 ◽

Author(s):

Min Zeng ◽

Yifan Wu ◽

Chengqian Lu ◽

Fuhao Zhang ◽

Fang-Xiang Wu ◽

...

Keyword(s):

Deep Learning ◽

Subcellular Localization ◽

Order Information ◽

Embedding Method ◽

Rna Molecules ◽

Complete Representation ◽

Learning Framework ◽

Link Type ◽

Non Coding Rna ◽

High Level

AbstractMotivationLong non-coding RNAs (IncRNAs) are a class of RNA molecules with more than 200 nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs can provide valuable insights into their biological functions. Existing computational methods for predicting lncRNA subcellular localization use k-mer features to encode lncRNA sequences. However, the sequence order information is lost by using only k-mer features.ResultsWe proposed a deep learning framework, DeepLncLoc, to predict lncRNA subcellular localization. In DeepLncLoc, we introduced a new subsequence embedding method that keeps the order information of lncRNA sequences. The subsequence embedding method first divides a sequence into some consecutive subsequences, and then extracts the patterns of each subsequence, last combines these patterns to obtain a complete representation of the lncRNA sequence. After that, a text convolutional neural network is employed to learn high-level features and perform the prediction task. Compared to traditional machine learning models with k-mer features and existing predictors, DeepLncLoc achieved better performance, which shows that DeepLncLoc could effectively predict lncRNA subcellular localization. Our study not only presented a novel computational model for predicting lncRNA subcellular localization but also provided a new subsequence embedding method which is expected to be applied in other sequence-based prediction tasks.AvailabilityThe DeepLncLoc web server, source code and datasets are freely available at http://bioinformatics.csu.edu.cn/DeepLncLoc/, and https://github.com/CSUBioGroup/[email protected]

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